Statistical Tables for Pocket PC 2002/2003

The Statistical Tables consist of  the 11 statistical distribution tables in this tools. You needn't take the thick statistics book anymore if you have this tool in your mobile phone. You can try various variables or parameters to compare the different conditions, which other programs do not allow you this flexibility. This is the powerful tools for students and statistics related person. The following distribution tables are in this tools now.

Normal Distribution. The normal distribution (the "bell-shaped curve" which is symmetrical about the mean) is a theoretical function commonly used in inferential statistics as an approximation to sampling distributions. In general, the normal distribution provides a good model for a random variable.

t Distribution. The t distribution is symmetric about zero, and its general shape is similar to that of the standard normal distribution. It is most commonly used in testing hypothesis about the mean of a particular population.

F Distribution. F distribution is most commonly used in tests of variance (e.g.,ANOVA). The ratio of two chi-squares divided by their respective degrees of freedom is said to follow an F distribution. The F distribution has the probability density function.

Chi-square Distribution. The chi-square distribution is most frequently used in the modeling of random variables in statistical applications. The sum of nindependent squared random variables, each distributed following the standard normal distribution, is distributed as Chi-square with ndegrees of freedom.

Binomial Distribution. The binomial distribution is useful for describing distributions of binomial events, such as the number of males and females in a random sample of companies, or the number of defective components in samples of 20 units taken from a production process.

Negative-Binomial Distribution. The negative binomial distribution is used when the number of successes is fixed and we're interested in the number of failures before reaching the fixed number of successes.

Poisson Distribution. The Poisson distribution is also sometimes referred to as the distribution of rare events. Examples of Poisson distributed variables are number of accidents per person, number of  sweepstakes won per person, or the number of catastrophic defects found in a production process.

Exponential Distribution. The exponential distribution is frequently used to model the time interval between successive random events. Examples of variables distributed in this manner would be the gap length between cars crossing an intersection, life-times of electronic devices, or arrivals of customers at the check-out counter in a grocery store.

Gamma distribution. The gamma distribution is probability density function of the exponential distribution has a mode of zero.In many instances, it is known a priori that the mode of the distribution of a particular random variable of interest is not equal to zero.

Geometric Distribution. If independent Bernoulli trials are made until a "success" occurs, then the total number of trials required is a geometric random variable.

Hypergeometric Distribution.The hypergeometric distribution arises when a random selection.